1. Strongest Agentic Coding Model

• State-of-the-art on Terminal-Bench 2.0 (82.7%), Expert-SWE (73.1%), and SWE-Bench Pro (58.6%), outperforming GPT-5.4 on complex coding tasks.
• Holds context across large systems, reasons through ambiguous failures, and carries changes through surrounding codebases with fewer tokens.

2. Higher Intelligence at GPT-5.4 Latency

• Co-designed, trained, and served on NVIDIA GB200/GB300 NVL72 systems to match GPT-5.4 per-token latency while performing at a significantly higher level.
• Uses fewer tokens to complete the same tasks, making it more efficient as well as more capable.

3. Powerful for Knowledge Work & Computer Use

• Scores 84.9% on GDPval (44 occupations) and 78.7% on OSWorld-Verified for autonomous computer operation.
• Excels at generating documents, spreadsheets, and reports; naturally moves across finding information, using tools, and checking output.

4. Scientific Research Co-Scientist

• Leading performance on GeneBench, BixBench, and FrontierMath; helped discover a new proof about Ramsey numbers verified in Lean.
• Strong enough to meaningfully accelerate progress at the frontiers of biomedical and mathematical research.

1. Purpose-Built for Agentic Coding

• Designed specifically for environments where the model acts as an autonomous or semi-autonomous coding agent.
• Optimized for multi-step reasoning in code tasks such as planning, refactoring, debugging, file generation, and tool coordination.

2. Enhanced Coding Intelligence

• Extends GPT-5.1's advanced reasoning capabilities to handle complex software architecture decisions.
• Better accuracy in code generation across languages (JavaScript, Python, TypeScript, Go, Rust, etc.).
• Produces cleaner, more idiomatic code aligned with modern frameworks and best practices.

3. Superior Tool Use & Code Navigation

• Excels at reading, understanding, and transforming multi-file codebases.
• Works well with Codex workflows that simulate real developer tooling.
• Strong at following function signatures, constraints, and code patterns within an existing project.

4. Long-Range Context Awareness

• 400,000-token context window enables the model to ingest large repositories or multiple files simultaneously.
• Supports deep analysis of project structures, dependencies, and cross-file logic.

5. Multi-Modal Development Capabilities

• Accepts text + image input and output - suitable for tasks like:
  • Reading UI mockups or screenshots to generate code
  • Understanding architectural diagrams
  • Reviewing images of whiteboard sessions

6. Agentic Workflow Optimization

• Built to manage longer chains of thought and execution typically required in:
  • Automated code repair
  • Project bootstrapping
  • Linting and migration tasks
  • Long-running coding agents using planning + execution loops

7. Continually Updated Model Snapshot

• Codex-specific version receives regular upgrades behind the scenes.
• Ensures the latest coding improvements without requiring developers to update model names.

8. Reliable Instruction Following

• Highly consistent in honoring explicit constraints:
  • Code styles
  • Folder structures
  • API contracts
  • Framework conventions

9. Broad API Support

• Works across Chat Completions, Responses API, Realtime, Assistants, and more.
• Ideal for apps that need live, reasoning-heavy coding agents or generative dev environments.